Field of the Invention
The present invention relates to a data processing method for learning a discriminator which performs predetermined discrimination on input data and a data discrimination method using the discriminator.
Description of the Related Art
In recent years, various methods have been proposed as a method for identifying a category of an input pattern or setting an input pattern as an explanatory variable so as to estimate an objective variable corresponding to the explanatory variable. Examples of the method include a method using a plurality of decision trees learned using a large number of learning data.
As represented by bagging trees disclosed in L. Breiman, “Bagging Predictors”, Machine Learning 26, pp. 123 to 140, 1996, for example, first, a plurality of learning data subsets are generated using a large number of learning data by bootstrap sampling and decision trees are learned using the learning data subsets. Then a method for discriminating an input pattern by integrating outputs relative to the input pattern of the learned decision trees has been proposed.
In general, in a discrimination method integrally using a plurality of discriminators, it is important for the discriminators to have variation. In the bagging trees, a variety of learning data subsets generated by the bootstrap sampling are used for learning of the decision trees so that a variety of decision trees may be generated. Therefore, discrimination results of the decision trees relative to the input pattern are integrated so that high performance may be realized.
As a method for further enhancing variation, L. Breiman, “Random Forests”, Machine Learning 45, pp. 5 to 32, 2001 discloses randomized trees in which branch rules of nodes of decision trees are selected at random when the decision trees are generated. By this, a variety of decision trees are generated although capabilities of the individual decision trees are low, and accordingly, high performance may be realized by using such decision trees.
Furthermore, in Japanese Patent Laid-Open No. 2010-287179, a discriminator generation method for enhancing capabilities of discriminators while enhancing variation of the discriminators is proposed. In this method, a relatively high selection probability is assigned to a discrimination rule which is effective for discrimination and discriminators are generated such that such a discrimination rule is preferentially selected. By this method, capabilities of the discriminators may be improved while variation of the discriminators is ensured, and consequently, high discrimination capabilities may be realized.
As described above, a method for learning discriminators having high discrimination capabilities while variation of the discriminators is enhanced is demanded as a method for learning a plurality of discriminators for performing discrimination using a plurality of discriminators.
The present invention provides a method for learning discriminators such that the discriminators have high discrimination capabilities while variation of the discriminators is ensured and provides a data discrimination method using the discriminators.